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Battery choice and management for New Generation Electric Vehicles / A. AFFANNI; A. BELLINI; G. FRANCESCHINI;GUGLIELMI P.; C. TASSONI. - In: IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS. - ISSN 0278-0046. -52:5(2005), pp. 1343-1349.

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Battery choice and management for New Generation Electric Vehicles

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IEEE

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 52, NO. 5, OCTOBER 2005 1343

Battery Choice and Management for New-GenerationElectric Vehicles

Antonio Affanni, Alberto Bellini, Member, IEEE, Giovanni Franceschini, Paolo Guglielmi, andCarla Tassoni, Senior Member, IEEE

Abstract—Different types of electric vehicles (EVs) have been re-cently designed with the aim of solving pollution problems causedby the emission of gasoline-powered engines. Environmental prob-lems promote the adoption of new-generation electric vehicles forurban transportation. As it is well known, one of the weakest pointsof electric vehicles is the battery system. Vehicle autonomy and,therefore, accurate detection of battery state of charge (SoC) to-gether with battery expected life, i.e., battery state of health, areamong the major drawbacks that prevent the introduction of elec-tric vehicles in the consumer market. The electric scooter may pro-vide the most feasible opportunity among EVs. They may be a re-placement product for the primary-use vehicle, especially in Eu-rope and Asia, provided that drive performance, safety, and costissues are similar to actual engine scooters.

The battery system choice is a crucial item, and thanks to an in-creasing emphasis on vehicle range and performance, the Li-ionbattery could become a viable candidate. This paper deals with thedesign of a battery pack based on Li-ion technology for a prototypeelectric scooter with high performance and autonomy. The adoptedbattery system is composed of a suitable number of cells series con-nected, featuring a high voltage level. Therefore, cell equalizationand monitoring need to be provided. Due to manufacturing asym-metries, charge and discharge cycles lead to cell unbalancing, re-ducing battery capacity and, depending on cell type, causing safetytroubles or strongly limiting the storage capacity of the full pack.

No solution is available on the market at a cheap price, becauseof the required voltage level and performance, therefore, a dedi-cated battery management system was designed, that also includesa battery SoC monitoring. The proposed solution features a highcapability of energy storing in braking conditions, charge equaliza-tion, overvoltage and undervoltage protection and, obviously, SoCinformation in order to optimize autonomy instead of performanceor vice-versa.

Index Terms—Batteries, electric vehicles (EVs).

I. INTRODUCTION

BATTERIES for high-performances electric vechicles(EVs) should be a nice tradeoff between “drive perfor-

mance” and “high reliability.” The former feature includesvechicle autonomy and power output, and the latter includes along lifetime, a maintenance-free system, high degree of safety,and energy regeneration capabilities. In order to achieve thesegoals the following characteristics are required for an ideal

Manuscript received June 3, 2004; revised November 18, 2004. Abstract pub-lished on the Internet July 15, 2005. This work was supported by the ItalianGovernment under COFIN 2000. This paper was presented at the 2003 IEEE In-ternational Electric Machines and Drives Conference, Madison, WI, June 1–4.

A. Affanni, A. Bellini, G. Franceschini and C. Tassoni are with the Depart-ment of Information Engineering, University of Parma, 43100 Parma, Italy.

P. Guglielmi is with the Department of Electrical Engineering, Politecnico diTorino, 10129 Turin, Italy.

Digital Object Identifier 10.1109/TIE.2005.855664

battery for an EV: high energy density, high output power (den-sity), long life, high charge–discharge efficiency, wide rangeof use from low temperature to high temperatures, minimalself-discharge, good load characteristics, good temperaturestorage characteristics, low internal resistance, no memoryeffects, fast charging, high degree of safety, high reliability, lowcost, and good recycleability, [1], [2].

Electric scooters, which address the “drive performance”issue, may provide a more feasible opportunity for Li-Ionbattery technology. Thanks to an increasing emphasis on ve-hicle range and battery energy density, Li-ion could become aviable candidate and, unlike nonelectric vehicles (NEVs) andelectric-assisted bicycles, electric scooters may be a replace-ment product for primary-use vehicles, especially in Europeand Asia. Lithium-ion rechargeable batteries are gaining pop-ularity over nickel-based rechargeable batteries, as the Li-ioncharacteristics are very attractive and unique. The greatestadvantages are the high cell voltage (3 : 1 compared with nickeltechnology) and superior energy density (roughly, 2 : 1). Otherattractive features are a very low self-discharge rate (about1 : 4 compared with nickel technology) and no memory effect.However Li-ion batteries are much less tolerant of abuse thannickel-based chemistries. Overcharging and overdischarginggreatly reduces cycle life. Worse yet, depending on cathodechemistry overcharging may cause battery overheating, venting,or even explosions. The overcharge voltage is within a fewpercent of the desired full-charge voltage, requiring accuratevoltage monitoring during charge to distinguish between fullcharge and overcharge [3].

In order to address this safety issue, battery protection cir-cuits must be properly designed and included within the Li-ionbatteries. The circuitry is designed into the battery “pack” andis normally transparent to the user. Its purpose is to monitor thebattery cell voltages and to prevent overcharge or overdischargeby opening the current path if a cell is out of the normal op-erating voltage range. Overload and short-circuit protection isalso provided, monitoring the discharge current and openingthe circuit if the current exceeds a pre determined threshold.There are many challenges in designing a practical battery pro-tection circuit. It must be small and economical enough for themanufacturer to include it inside the battery pack. Moreover,in order to minimize impact on battery life, the circuit’s cur-rent consumption must be significantly lower than the inherentself-discharge rate of the battery, since it is powered continu-ously. The cell voltage monitoring circuit must be very accu-rate to allow a full charge while preventing an overcharge. If theovercharge threshold is too low, it will prevent the battery from

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1344 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 52, NO. 5, OCTOBER 2005

TABLE ISINGLE CELL VERSUS BATTERY PACK

being fully charged, reducing operating time between charges.If the threshold is too high, the battery may be damaged by anovercharge [4]–[6]. In order to store the maximum amount ofenergy, charge equalization of every cell would be required, andit becomes mandatory in the case of cell imbalance due to in-trinsic manufacturing differences or repeated charge/dischargecycles. Toward this aim, suitable equalization circuits must beadded for every single cell.

II. BATTERY PACK DESIGN

The developed battery pack consists of two independent bat-teries parallelly connected through two dc/dc converters to acommon dc link. Each battery is constituted of 70 LiCoO cellsseries connected for a nominal battery voltage of about 260 V.Table I reports the characteristics of a single cell and of thewhole battery pack. Fig. 1 shows the battery stack: seven packsconnected in series constitute the two batteries. The ten cells in-side every pack are individually protected and equalized by asuitable electronic circuit.

As is known, charge and discharge cutoffs for the lithium-ionbattery cells must be closely controlled or early cell damage willoccur: Overcharge leads to electrolyte oxidation and decompo-sition while overdischarge results in cathode structural changes.The high number of adopted cells (70) stacked in series com-pounds the problem: under these conditions individual cell mon-itoring and equalization must be provided.

A. Cell Voltage Monitoring

According to manufacturer specifications the cell chargemust be stopped if voltage exceeds 4.2 V and, additionally,cell discharge will be stopped if cell voltage falls below the2.7-V threshold. Obviously, from the energy storage point ofview, stopping battery charge when the first cell supasses thevoltage threshold is not an efficient solution. This would resultin storing an amount of energy clamped by the leakiest cell ofthe stripe. In order to optimize EV autonomy, a proper chargeequalization must be provided. In fact, the voltage profileduring discharge may be remarkably different for each cellwithin a ten-cell stripe battery pack.

The cell voltage monitoring circuit consists of a precisiontemperature compensated voltage reference and of a resistorvoltage divider to sense cell voltage. The voltage of each cell

is continuously compared with voltage reference to state cellvoltage condition. Suitable threshold hysteresis was imple-mented for both overvoltage and undervoltage conditions toprevent oscillation due to battery equivalent series resistance(ESR). Parasitic elements must be taken into account duringtransient conditions because voltage drop can deceive theprotection circuit causing false undervoltage. RC low-passfilters with suitably large time constant were used to addressthis issue. Dedicated circuits were designed for overvoltageand undervoltage monitoring. High resistance values were usedin order to decrease power consumption. Moreover, resistancetolerance is very important to achieve accurate protection: SMDresistors 0805 series with 0.5% tolerance values were used ex-tensively. The normal operating voltage range of each cell is [3V, 4.15 V]. When the voltage exceeds this range, undervoltageand overvoltage protection circuits start to operate, sendingthe actual cell voltage level to the control by an optocoupler.Specifically, in the overvoltage operation range [4.15 V, 4.25V], the overvoltage circuit produces a linear voltage dependingon the charge state of the relevant cell.

With a dual behavior in the undervoltage operation range[2.7 V, 3 V], the undervoltage circuit produces a linear voltagedepending on the charge state of the relevant cell. When two ormore cells are operating in a nonoptimal voltage, the controlboard acts as an ex-or and sends the output of the sensing circuitwhich shows the worst case of charge state. A temperature sensorensures that the battery pack temperature does not surpass 70C, in order to prevent damage to the cells. The outputs of the

above-mentioned protection circuits are sent through a properinterface (implemented with an array of optocouplers) to themotion control digital signal processor (DSP) allowing a tuningcontrol strategy according to journey characteristics. Each sensorobserves the voltage across the relevant cell independently of theposition of the cell inside the pack. The reason for the differentslopes betweenovervoltage andundervoltage transitions is due tothe different priority of intervention. In overvoltage conditions,the DSP must stop the charge at constant current starting thecharge at constant voltage. On the other hand, when a cell isin undervoltage condition the DSP must reduce the currentrequested by the user providing less acceleration in order toguarantee the best autonomy of the vehicle and allowing alarger margin of battery safety.

B. Cell Equalization

To obtain maximum energy storage, cells must be individu-ally equalized during charge operation. Toward this aim, in theliterature several solutions are adopted. The most widely usedare critically reviewed in Table II.

In spite of higher energy efficiency, the use of reactiveswitched elements was discarded because it leads to a bulkysolution. The switching resistive solution was adopted. As faras the choice of the switch is concerned, the need for goodbalancing capability and especially for high reliability leads toan automotive technology solution. In order to obtain a simpleand reliable solution a protected FET (ProFET) [13] was used inparallel to each cell of the pack to drain current from the cells atthe threshold of overvoltage (4.15 V). In overvoltage conditionsthe battery charge will continue without overcharging the cell

AFFANNI et al.: BATTERY CHOICE AND MANAGEMENT FOR NEW-GENERATION ELECTRIC VEHICLES 1345

Fig. 1. Battery cells hierarchical structure.

TABLE IIEQUALIZATION CIRCUIT CHARACTERISTICS

because of the shunting operation performed by the ProFET.Battery charging will be stopped when the cell overvoltagemonitoring circuit detects a voltage of about 4.25–4.3 V. TheProFET is a smart FET, which includes a high-side drive andis fully protected by embedded protection functions, thus, thissolution greatly simplifies the equalization circuit. Table IIIreports ProFET current capabilities. A dedicated circuit forequalization purposes was designed for each cell inside thebattery pack relying on ProFETs (see Fig. 2).

The choice of the components of the control circuit wasdriven by the need of low power consumption of the board(500 mW). Because of the large number of devices, theywere placed on two faces in a four-layer printed circuit board(PCB). The conformation and the size of the board are relatedto the size of the battery pack composed of ten cells; infact, the board is mounted on the battery pack, in order toreduce the volume. The size of the board is 201 mm 54mm (7.91 2.12 in). SMT components were used for sizeand precision purposes.

The pads, which provide the voltage of each cell, were placedin order to reach the cell connectors by screwed bars for ease ofassembly.

TABLE IIIProFET CHARACTERISTICS

III. BATTERY MANAGEMENT

Batterystatecanbeachievedifbatterystateofcharge(SoC)andbattery state of health (SoH) can be accurately monitored [14].

A few experiments were made to define an accurate and re-liable procedure for SoC detection. As already stated, batteriesare one of the weakest parts of an EV. Limited autonomy andrecharge facilities are some of the main drawbacks. Therefore,the full exploitation of the battery is a key element, and this re-quires an accurate knowledge of the actual SoC.

Accurate estimation of SoC is a complex task. To accomplishit, a few approaches are possible: circuit models, empiricalmodels, statistical or artificial-intelligence-based models [8],[9], [7], [10]. Circuit models require much work for each typeof battery and require nonlinear elements. Classical methodsfor SoC estimation include: measurements of the extractedcharge, measurements of the battery internal impedance or

1346 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 52, NO. 5, OCTOBER 2005

Fig. 2. Single-cell equalization circuit.

Fig. 3. 1.5-Ah battery cell R profile.

resistance, and measurements of the battery no-load voltage.Though none of these provides acceptable results, a hybridmethod, including measurements of internal impedance orresistance ( ), extracted charge ( ), and no-load voltage( ), can achieve a better estimation of SoC. Fig. 3 refers toa 1.5-Ah battery cell; it shows the profile as a function oftime: the profile is almost constant during battery discharge andit suddenly increases close to the end of discharge. This profilealone is not sufficient to evaluate the SoC. Fig. 4 refers to a10-Ah battery cell; it shows a discharge profile as a function oftime at constant discharge current, equal to the battery capacity.

can be computed using this profile, however, this quantityis not sufficient to state SoC. The knowledge of the amount ofenergy that can be extracted from the battery is a key elementin order to optimize system performance. This information canbe retrieved starting from SoC only if battery discharge statusis fully defined. A battery can be defined as being completelydischarged when it cannot provide the power needed by the

Fig. 4. 10 Ah battery cell discharge profile.

system it supplies. Therefore, an accurate estimation of SoCshould be made only by fully discharging the battery. Thisprocedure is impractical because of the large amount of time re-quired. The authors’ assumption is that SoC decreases linearlywith time if discharge occurs with a periodical current profile.This simple approximation was then verified a posteriori bylaboratory tests. Starting from this assumption, the authorspropose a neural-network (NN)-based procedure that allowsthe estimation of battery SoC mapped as a function of ,

, and .The aim is to find a function so that

. As stated before can be obtained froman analytical study of the physical and chemical behavior ofbatteries, or it can be mapped from empirical data. The latterapproach turns out to be preferable, since it is simpler andoften more efficient. In this paper, an empirical estimation of

was made, using an NN, which maps the input vector: ,, into the SoC [7], [10].

AFFANNI et al.: BATTERY CHOICE AND MANAGEMENT FOR NEW-GENERATION ELECTRIC VEHICLES 1347

Fig. 5. Typical discharge current profile as a function of time for a single cell.

As usual, the NN estimation is more accurate if the trainingset is large and fully representative of different operating con-ditions. Therefore, the behavior of was analyzed in severaldifferent conditions, performing several different discharge cy-cles. Specifically, the test setup must allow discharging thebattery cell with a given current to measure cell voltage andcurrent during discharge. Fig. 5 shows a current profile chosenaccording to the typical EV urban duty cycle for a single cell.The test setup for the experiments was made by a programmablecurrent generator, a data-logger, and a host PC with IEEE-488interface and suitable LabView software. Two graphical userinterfaces (GUIs) were designed in LabView which allow usto specify the desired current profile during battery discharge,to set up the measurements, and to store collected data onthe host PC. Starting from measured data (battery current andvoltage during discharge) a MATLAB [12] script is used toproduce the training set. is computed with the compositetrapezoidal approximation from current sampling. is com-puted measuring the voltage drop corresponding to a currentvariation. Finally, was estimated directly as the voltagewith no load in time intervals where the current is zero. TheSoC values suitable for NN training are obtained in accordancewith the previous assumption on cell discharge behavior.

The collected training data were used to train a two-layer NNwith tansig activation function. The learning procedure is basedon the Broyden, Fletcher, Goldfarb and Shanno (BFGS) algo-rithm [11], [12].

ThetrainedNNwasthenusedasareal-timeestimatorofthebat-tery SoC. In order to validate the estimation results, different testswere performed changing the discharging conditions. Specifi-cally, the current profile was varied during every validation testin order to prove the NN performance in operations close to thereal ones. Each test was stopped after a consistent number ofcycles, the battery was fully discharged, and the measure of theextracted charge was compared with the NN output. Two dif-ferent Lithium-ion battery cells were used. As an example, Fig. 6shows the battery SoC estimated by NN compared with the ac-tual SoC measured during the discharge of a Lithium-ion battery.The curves are almost completely overlapped.

Fig. 6. Neural network estimation (light grey) compared with current SoC asa function of time cycles.

Fig. 7. Prototype battery pack.

Several discharge cycles were repeated with the described testsetup, obtaining a good agreement betweenNN estimation andmeasured results, for different tests performed with differentdischarge current profiles.

The SoC information allows the tuning control strategy of theEV according to journey characteristics. This leads to the opti-mization of battery lifespan instead of useless performance inthe case of an urban journey. The developed method is based onan NN estimator in order to comply with the nonlinear behaviorof the battery, providing a simple and effective implementation.Experiments were made on different discharge profiles modeledon the typical urban EV duty cycle [15].

IV. EXPERIMENTAL RESULTS

In order to verify the design of battery management circuitsshown in the previous sections a prototype battery pack wasrealized, including battery protection and SoC detection (seeFig. 7).

The realized battery pack includes detection of battery SoCallowing different control strategy depending on journey or

1348 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 52, NO. 5, OCTOBER 2005

Fig. 8. Photograph of the switching transformer.

Fig. 9. Voltage reference output (black) and its ac ripple residual (light grey).

driver requirements. These battery packs will be used to supplya high-performance electric scooter [15]. The specificationsof the electric scooter are: 6-kW motor; 5 kWh; 130 km ofautonomy at 50 km/h.

In order to minimize battery consumption and to ensureproper operation of monitoring circuits even in the case of lowcell voltage, the power supply is external. The voltage supplywaveform is a trapezoidal wave 30 V peak to peak, at high fre-quency (100 kHz) [16], [17]. In order to obtain correct voltagesfor electronic components a suitable switching transformer wasrealized (see Fig. 8).

The switching transformer supplies a voltage doubler circuitand an ac/dcconverter. Becauseof the required voltage resolutiona low supply ripple is mandatory. The residual ripple at the outputof the ac/dc converter at full load is about 50 mV peak to peak.

Fig.9reports theoutputofthevoltagereferenceandtheresidualac ripple. As previously stated, voltage reference stability is a keyelement for the accurate monitoring of the cell voltage.

The figures now cited show overvoltage and undervoltage cir-cuits behavior. Specifically, Fig. 10 shows the drained currentversus cell voltage characteristic. Fig. 11 shows overvoltage cir-cuit behavior: if the cell voltage surpasses the threshold (4.15V) the ProFET starts draining current and, eventually, when theproFET runs thermally and cell voltage rises to 4.25 V the mon-itoring output becomes low, stopping battery charge. The resultsshown in the figure are obtained using a programmable voltage

Fig. 10. Shunted current, cell voltage characteristic.

Fig. 11. ProFET current (top trace). Cell voltage (middle trace) overvoltagecircuit output (bottom trace).

Fig. 12. Cell voltage (light gray trace) and undervoltage circuit output (blacktrace).

source to simulate battery behavior. Fig. 12 shows undervoltagemonitoring.

AFFANNI et al.: BATTERY CHOICE AND MANAGEMENT FOR NEW-GENERATION ELECTRIC VEHICLES 1349

As can be seen, when the cell voltage falls below 3.12 V theoutput of the undervoltage circuit starts to decrease down to alow level in correspondence to a cell voltage of 2.79 V, i.e., thelower threshold.

V. CONCLUSION

A simple approach for protection and estimation of SoC ofLi-ion batteries tailored for EV applications has been presented.The proposed estimation is based only on the measurementof electrical quantities like , , and , which are usu-ally available without introducing further invasive sensors.Electronic circuits for charge equalization, undervoltage andovervoltage monitoring, and temperature sensing were de-signed and implemented on a dedicated PCB. Their outputsdrive motor control strategy according to journey character-istics. The implemented charge equalization strategy allowsfor obtaining the maximum energy storage also during vehiclebraking operation. The proposed control board was embeddedin a prototype electric scooter in order to increase its reliabilityand autonomy.

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[4] D. Salerno and R. Korsunsky, “Practical considerations in the design oflithium-ion battery protection systems,” in Proc. IEEE APEC’98, vol. 2,1998, pp. 700–707.

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[6] H. Tsukamoto, “High reliability lithium rechargeable batteries for spe-cialties,” IEEE Aerosp. Electron. Syst. Mag., vol. 18, no. 1, pp. 21–23,Jan. 2003.

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[12] MATLAB. Neural Networks Toolbox, The MathWorks Inc., Natick, MA,2004.

[13] “BSP 772 T Smart Power High-Side-Switch,” Infineon TechnologiesAG, Munich, Germany, 2004.

[14] A. Affanni, A. Bellini, C. Concari, G. Franceschini, E. Lorenzani, and C.Tassoni, “EV battery state of charge: Neural network based estimation,”in Proc. IEEE IEMDC’03, vol. 2, Madison, WI, Jun. 2003, pp. 684–688.

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[16] A. Fratta, P. Guglielmi, G. M. Pellegrino, and F. Villata, “DC-AC con-version strategy optimized for battery or fuel-cell-supplied AC motordrives,” in Proc. IEEE ISIE’00, vol. 1, 2000, pp. 230–235.

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Antonio Affanni was born in Italy in 1977. He re-ceived the Master’s degree in electronic engineeringin 2003 from the University of Parma, Parma, Italy,where he is currently working toward the Ph.D. de-gree.

His current research interests include sensors forindustrial applications and high-performance electricdrives.

Alberto Bellini (S’96–M’04) was born in Italy in1969. He received the Dr.Eng. degree in electricalengineering and the Ph.D. degree in computer sci-ence and electronics engineering from the Universityof Bologna, Bologna, Italy, in 1994 and 1998,respectively.

Since 1999, he has been with the Universityof Parma, Parma, Italy, where he is currently anAssistant Professor of Electrical Engineering. Hisresearch interests include power electronics, signalprocessing for audio and industrial applications, and

electric drive design and diagnosis. He has authored or coauthored more than70 papers and is the holder of three industrial patents.

Giovanni Franceschini was born in Reggio Emilia,Italy, in 1960. He received the Master’s degreein electronic engineering from the University ofBologna, Bologna, Italy.

In 1990, he joined, as an Assistant Professor, theDepartment of Information Technology, Universityof Parma, Parma, Italy, where he currently is anAssociate Professor. His research interests includehigh-performance electric drives and diagnostictechniques for industrial electric systems. He hasauthored or coauthored more than 100 technical

papers and is the holder of three industrial patents.

Paolo Guglielmi was born in Imperia, Italy, in1970. He received the M.Sc. degree in electronicengineering and the Ph.D. degree in electrical engi-neering from the Politecnico di Torino, Turin, Italy,in 1996 and 2001, respectively.

In 1997, he joined the Department of Electrical En-gineering, Politecnico di Torino, where he became aResearcher in 2002. His fields of interest are powerelectronics, high-performance servo drives, and com-puter-aided design of electrical machines. He has au-thored several papers published in technical journals

and conference proceedings.Dr. Guglielmi is Registered Professional Engineer in Italy.

Carla Tassoni (SM’97) was born in Bologna, Italy,in 1942. She received the Master’s degree in elec-trical engineering from the University of Bologna,Bologna, Italy, in 1966.

She joined the University of Bologna as an As-sistant Professor and then became an Associate Pro-fessor of Electrical Machines in the Department ofElectrical Engineering. She is currently a Full Pro-fessor of Electrical Engineering at the University ofParma, Parma, Italy. She has authored or coauthoredmore than 100 scientific papers and is the holder of an

industrial patent. Her main research interests include the simulation and mod-eling of electric systems and applications of diagnostic techniques.

Prof. Tassoni is a Membr of the European Consortium for Research on Con-dition Monitoring of Electric Systems and Drives (CRCM).